Data management for field and monitoring data

Years of fieldwork tend to end up in dozens of spreadsheets: column names that change between years, the same species written three ways, dates in mixed formats. Before any of it can be analysed, it has to be in one place and consistent.

What I can do

One database: spreadsheets and field sheets brought together in a structured SQL database.
Consistent names and codes: sites, methods and species standardised, with species names checked against a reference list (for marine species, WoRMS).
Validation checks: rules that catch typos, impossible values and missing fields when new data is added.
Documentation: a data dictionary that explains every table and field.
Access: simple dashboards (Power BI or Streamlit) so the team can explore the data without writing code.

How a project runs

A short call about the question, the data you have and when you need it.
A look at a sample of the data, then a written proposal with the approach, its limits and the time it will take.
The work itself, with the decisions written down as I go.
Delivery and a walk-through of the results, plus the files and code.

What you get

The database, with the cleaned historical data loaded.
The scripts used to clean and load it, so new data follows the same path.
A data dictionary and a short handover session.

Background

I work with Python and SQL to clean and structure data, and I have worked with field records from ecological surveys and with process data in manufacturing.

Tell me about your data.

A few lines are enough: the question, the data you already have and when you need it. I reply in English, Portuguese or Spanish.

Write to me